The present invention relates to learning of a one-class classifier. More specifically, this invention relates to rapid on-line one-class learning.
One class learning tries to distinguish one class of objects from all possible objects by learning from a training set containing only objects of that class. Fast methods for training support vector machines (SVMs) such as applied in one-class learning problems focus almost exclusively on heuristics for bringing down the cost of large quadratic programming problems. Divide-and-conquer training approaches typically break the problem into subproblems corresponding to subsets of training data, while iterating the composition and coverage of the subproblems relative to the overall set of examples, and extend partial solutions to cover the entire data set. In this iterative process they repeatedly solve quadratic programming problems of much smaller size. Successful approaches, such as sequential minimal optimization (SMO) type learning use a large number of iterations in effect.
Current support vector machines (SVM) recalculate support vectors based on new data and old primary data, requiring the learning process to have access to old primary data and making the learning process very computing intensive. Primary data is data received by a processor from which object features can be learned.
Accordingly, novel and improved systems and methods to perform rapid on-line learning without requiring access to old primary data, are required.